None.
The present invention relates to the field of Artificial Intelligence (AI) and the use of Interactive Computer Systems, Computational Linguistics and Natural Language Processing. More particularly, this invention comprises methods and apparatus for modeling human-like interactions on a computer for commercial applications.
Introduction
The French philosopher-mathematician Renxc3xa9 Descartes in 1637 predicted that it would never be possible to make a machine that thinks as humans do. British mathematician and computer pioneer Alan Turing in 1950 declared that one day there would be a machine that could duplicate human intelligence in every way.
By the early 1990""s, Artificial Intelligence (AI) itself had not been achieved, but logic programs called expert systems were devised to allow computers to xe2x80x9cmake decisionsxe2x80x9d by interpreting data and selecting from among alternatives. Technicians can now run programs used, for example, in complex medical diagnosis, language translation, mineral exploration and computer design.
Computers can outperform mental functions in limited areas, notably in the speed of mathematical calculations. Most computers operate by logic steps or algorithms. They do serial processing; operations of recognition and processing are performed one at a time. The brain appears to do parallel processing, that is, performing operations simultaneously. Critics of the computational approach insist that a person who solves a problem indicates understanding, something that solving a computation does not indicate. Some proponents, therefore suggest that for computers to duplicate human reasoning which involves not only logic but perception, awareness, emotional preferences, values, ability to generalize, etc., they must be patterned after the brain, which essentially comprises a network of nerve cells.
However, there is no universally accepted theory of human intelligence. Some researchers have even suggested that our current knowledge of fundamental physics, that is, the language that we use to describe and understand the universe, is not adequate to describe the complexity of the human brain. If this were true, it would seem that any hope of developing artificial intelligence is doomed to failure. While this postulate may be at least partially correct, and it is even possible that some human capabilities arise from the non-local aspects of the quantum field, the inventor believes that most aspects of human intelligence can be modeled and that these aspects can be simulated by implementation on conventional computers to do useful tasks. The present invention comprises such modeling and implementation.
Background Technology
The present theory of intelligence abandons many, if not most, of the assumptions of conventional technology made in the last fifty years by the AI community.
Most of the conventional technology starts with the recognition that an important feature of human intelligence is its ability to construct and make sense of strings of symbols, that is language. Many have assumed there is a syntactic xe2x80x9cparser enginexe2x80x9d in the human brain that mysteriously decodes strings of symbols into meanings and that this capability is somehow built into the brain. This is called the Universal Grammar (UG) theory. As evidence of this, many researchers point to papers that xe2x80x9cprovexe2x80x9d that the learning of language is impossible since it is too complex and confusing to be learned inductively. Some researchers have claimed that even scientists cannot understand grammar without first reading papers on the subject.
The problem of providing a practical, veracious method and apparatus for simulating human intelligence has presented a major challenge to the artificial intelligence community. The development of such a method and system that offers significant commercial benefits would constitute a major technological advance and would satisfy a long felt need in the information, communications, entertainment and many other businesses.
The present invention includes methods and apparatus for simulating human intelligence using natural language processing. The invention comprises:
(1) a cognitive model of human intelligence;
(2) a mathematical model of information abstraction and synthetic dialog interaction;
(3) a method of language-independent computer learning through training, interaction and document reading; and
(4) a method of efficient computer implementation of all preceding parts.
A novel theory of human intelligence is developed that is concrete and practical enough to be incorporated into machines that employ intelligent, directed use of language. The methods and apparatus disclosed provide enabling information to implement the theory in a conventional computer.
The cognitive model is a theoretical basis of the entire invention. It describes the way humans learn and interact in general terms. The mathematical model of information abstraction and synthetic dialog interaction and method of language-independent computer learning through training, interaction and document reading provide a mathematical basis for natural language learning and interaction between humans and a computer. It also provides the basis for machine translation from one language to another, the detection of patterns of speech for the purpose of identification, and provides the basis for personality simulations.
A working prototype of an xe2x80x9cAutomated Dialog Adaptive Machinexe2x80x9d (ADAM) has been created. The cognitive model of human intelligence is referred to herein as the Associative Abstraction Sensory Model (AASM). The description of the invention is organized onto three parts: (1) a description of the theory of intelligence that the computer algorithms are based on; (2) a mathematical model and (3) a computer implementation.
Using the AASM in the present invention, it is shown that pattern recognition, associative capabilities and sensory memory of the human brain alone can be used to describe the ability of humans to use ideas and language effectively.
Many working in the cognitive science and AI fields have assumed that cognition involves the encoding into the brain an unknown deep representation of knowledge sometimes called xe2x80x9cmentalese.xe2x80x9d Language production is seen as decoding mentalese into strings of symbols and language understanding as coding mentalese from symbols. Therefore cognition must reside in a hidden, unknown mechanism of the human brain. No such assumption is made in the AASM. The model does not require that hidden mechanisms are necessary to explain human comprehension.
The model posits that human-like intelligent behavior comes from the language itself. That is, it is the ability of humans to use language, i.e. strings of symbols, as representations of meaning in combination with other characteristics of the brain that define human intelligent behavior. How language is combined with other sensory information is the key to describing a working model of intelligence as well as reproducing it on a computer. The description of this process lies at the heart of the AASM.
Much previous work by others has concentrated on the encoding and decoding of symbol strings in a particular target language. For example, Noam Chomsky""s book, Syntactic Structures, is the classic work of transformational grammars and also the work of Terry Winograd (1971, 1972) who created the precursors of today""s commercial language interfaces. We are taught in school that only certain symbol sequences are correct and rules that describe xe2x80x9clegalxe2x80x9d sequences are called a grammar. A large portion of every human being""s education is learning the correct rules of their native language. Yet it is well known that children can produce legal sequences without formal training. A discussion of this may be found in Chapter 4 of Symbolic Species by Terrence W. Deacon who also has a very complete description of the origins of the Universal Grammar Theory. Simply being exposed to others that speak a particular grammar is enough for the average child to generate grammatical sentences. Yet almost the entire body of work in computational linguistics and artificial intelligence requires recognizing parts of speech as defined in formal grammars.
The AASM does not require knowing parts of speech, and, in particular, specialized knowledge of any kind. The capabilities of the AASM follow from the modeling of cognition and language learning, not any specialized knowledge of a target language, or any other knowledge for that matter.
An appreciation of other aims and objectives of the present invention and a more complete and comprehensive understanding of this invention may be achieved by studying the following description of a preferred and alternate embodiments and by referring to the accompanying drawings.